Abstract The increasing complexity of software development has prompted a shift in project management practices toward agile methodologies, often supported by specialized software tools for planning and reporting. However, many widely adopted tools offer only limited decision support capabilities. This work introduces ProjectION, an intelligent software tool designed to enhance decision-making in agile project management through techniques relative to the Search-Based Software Engineering field. Based on an in-depth analysis of the challenges faced in agile environments, ProjectION assists decision makers in monitoring project status, forecasting progress, and automating software release planning. A comprehensive usability study was conducted with a small sample of experienced IT professionals, following the ISO/IEC 25062:2006 Common Industry Format. Key usability metrics of effectiveness, efficiency, and user satisfaction were assessed. Despite minor usability issues, results indicate that ProjectION effectively supports agile release planning. A second experiment further demonstrates the tool’s utility in generating optimal release plans, outperforming manual solutions proposed by decision makers. To foster collaboration and future development, the core algorithms and execution service have been made publicly available, enabling integration of novel approaches to the Next Release Problem.

Agile methodologies are widely adopted in the industry, with iterative development being a common practice. However, this approach introduces certain risks in controlling and managing the planned scope for delivery at the end of each iteration. Previous studies have proposed machine learning methods to predict the likelihood of meeting this committed scope, using models trained on features extracted from prior iterations and their associated tasks. A crucial aspect of any predictive model is user trust, which depends on the model’s explainability. However, an excessive number of features can complicate interpretation. In this work, we propose feature subset selection methods to reduce the number of features without compromising model performance.To ensure interpretability, we leverage state-of-the-art explainability techniques to analyze the key features driving model predictions. Our evaluation, conducted on five large open-source projects from prior studies, demonstrates successful feature subset selection, reducing the feature set to 10% of its original size without any loss in predictive performance. Using explainability tools, we provide a synthesis of the features with the most significant impact on iteration performance predictions across agile projects.

Release planning is a critical step in the development of a software product, and it involves many factors. Deciding what to build for the next software release requires taking into account not only the cost of building a subset of software features, but also the expected satisfaction of the clients, as well as the dependencies that the features might have among them. This problem, called Next Release Problem, can be difficult to tackle by expert judge, or even intractable if the number of requirements, dependencies and clients to consider is very large. In the literature, this problem has been approached from the so-called search-based software engineering field, introducing a variety of metaheuristic algorithms to obtain a subset of release proposals that simultaneously optimise both cost and satisfaction. In this work, we present a GRASP-based advanced method, and evaluate it against other families of algorithms commonly applied to this problem, using two public and four synthetic datasets for the evaluation. Results show that solutions obtained by our proposal are superior to those of other algorithms in terms of quality indicators and speed of execution. Algorithms, datasets and evaluation framework have been made available to the research community.

In the development of a software product, the Next Release Problem is the selection of the most appropriate subset of requirements (tasks) to include in the next release of the product, such that the selected subset maximises the overall satisfaction of the stakeholders and minimises the total cost. Furthermore, in most cases, requirements or tasks cannot be developed independently, as there are dependencies between them, which must be respected in the selection for the next release. In this paper, we approach the Next Release Problem as a constrained bi-objective optimisation problem. The main contribution is the design of an Estimation of Distribution Algorithm that exploits domain knowledge, i.e. the dependencies between the requirements, to define the structure of a Bayesian network that models the relationships between the binary variables (requirements) to be optimised. The use of a Bayesian network with a fixed structure reduces the complexity of the search process, since it is unnecessary to learn the structure at each iteration of the algorithm. Moreover, it ensures that the sampled individuals are always valid with respect to the required dependencies. The second main contribution is the generation of a corpus of synthetic datasets with cost estimations derived from agile and classic management methodologies. Standard multi-objective metrics are computed in order to assess our proposal and compare it with other evolutionary multi-criterion optimisation algorithms, determining that it is the optimal choice when dealing with complex datasets.